code: def main(
      working_directory,
      output_file,
      task_id,
      seed,
      model,
      time_limit,
      per_run_time_limit,
  ):
      # vanilla and metalearning must be called first before ensemble and
      # meta_ensemble can be called, respectively.
      if model == "vanilla":
          run_experiment(
              working_directory,
              time_limit,
              per_run_time_limit,
              task_id,
              seed,
              use_metalearning=False,
          )
          score_ensemble.main(
              working_directory,
              output_file,
              task_id,
              seed,
              ensemble_size=1,
          )
      elif model == "metalearning":
          run_experiment(
              working_directory,
              time_limit,
              per_run_time_limit,
              task_id,
              seed,
              use_metalearning=True,
          )
          score_ensemble.main(
              working_directory,
              output_file,
              task_id,
              seed,
              ensemble_size=1,
          )
      else:
          score_ensemble.main(
              working_directory,
              output_file,
              task_id,
              seed,
              ensemble_size=50,
          )
  
file_path: auto-sklearn/scripts/2015_nips_paper/run/run_auto_sklearn.py
imports: import os
  import argparse
  import numpy
  import openml
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.metrics import balanced_accuracy
  from remove_dataset_from_metadata import remove_dataset
  import score_ensemble
label_desc: Use image node_id 3 for calling a global function with example usage: main(working_directory, output_file, task_id, seed, model, time_limit, per_run_time_limit) without return types